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Malaria transmission modelling: a network perspective

DOI: 10.1186/2049-9957-1-11

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Please see Additional file 1 for translations of the abstract into the six official working languages of the United Nations.Malaria transmission is challenging to model; its vector can be quite complex due to topographical and climatic variations as well as human mobility [1]. One of the United Nations (UN) Millennium Development Goals is to “have halted by 2015 and begun to reverse the incidence of malaria” which annually causes ~1 million death or >1 death every 30–60 second [2,3]. World Health Organization (WHO) has suggested that the most important measure is a timely response with the implementation of effective interventions once it has been detected [4]. This requires an effective monitoring/surveillance system that can provide long range forecasting, early warning, and early detection [5]. Towards this end, malaria transmission patterns will be informative in performing such surveillance functions.This article provides a review of related work on how to develop a computational means for inferring malaria transmission networks in populations, which incorporates: (1) partial surveillance data over time, i.e., the temporal-spatial distributions of cases of infection, and (2) infection models of malaria. A transmission network to characterize the temporal-spatial patterns of disease transmission, or a temporal-spatial disease transmission network, consists of a set of nodes and a set of links that connect them, where the nodes correspond to spatial locations, such as villages, with reported/observed disease incidences over time, and the directional links connecting the nodes correspond to the probability/likelihood of disease “transmission” from one node to another over time, e.g., hidden pathways of malaria transmission.Technically, the problem of computationally inferring malaria transmission networks is both interesting and challenging because, during the process of disease spread, the reported infection cases do not directly reflect the full extent of transm


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